Bibliographic record
Abstract
In Homer’s epic, The Odyssey, the author places Clytemnestra in stark opposition to Penelope, the wife of the epic’s hero, Odysseus. Clytemnestra, the wife of King Agamemnon, cheated on her husband and killed him upon his return from the Trojan war; an action that placed her in the category of a ‘bad wife.’ In contrast, Penelope uses her autonomy to stay within the traditional social roles of a good Greek wife. Penelope is compared with Clytemnestra and found equal to her, yet above her in morality – for she never betrays Odysseus. Even though Penelope does not act like Clytemnestra, the consequences of Clytemnestra’s action damage the reputation of not only Penelope but of all women. Despite his trust in Penelope, Odysseus treats her with suspicion until the end of the epic – as if she too may betray him. This paper will explain how the legacy of Clytemnestra’s actions impacted Penelope throughout the rest of the epic. In order to fully contextualize the power of Clytemnestra’s actions, this paper will analyze how the literary representation of women in classical works expressed the belief that women by nature behaved like Clytemnestra. Regardless of the faithfulness of Penelope, she remains under the cloud of a bad wife because all women – even good ones – cannot be trusted. Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Benjamin Garstad Department: History
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".